Accenture Global Solutions Limited

International Business Machines Corporation

Jeremiah Patrick Hayes

Nicholas McCarthy

Freddy Lecue

Qurrat Ul Ain

Mykhaylo Zayats

Xu Zheng

Luca Costabello

William Karol Lynch

Chahrazed Bouhini

Raymond Lloyd

Harry Roger Kolar

Christophe Dominique Marie Gueret

Rory McGrath

Penelope Daphne Tsatsoulis

Julianne Madeleine Chaloux

Mohit Aggarwal

Harshdeep Harshdeep

A device processes a knowledge graph to select a set of triples and generates a first class based on the set of triples. The device determines a quantity of quasi-identifier attributes in the first class and compares the quantity to a predefined parameter. The device embeds the knowledge graph to generate an embedding space representation, identifies a second class, and determines a first quantity of nodes in the first class and the second class. The device compares the first quantity to the predefined parameter and identifies a third class. The device determines a second quantity of nodes in the first class and the third class and compares the second quantity to the predefined parameter. The device merges the second class or the third class with the first class, based on the comparisons, to generate anonymized nodes for the knowledge graph.

Applying a k-anonymity model to protect node level privacy in knowledge graphs and a differential privacy model to protect edge level privacy in knowledge graphs

A device may receive a knowledge graph and SMILE data identifying compounds, and may train embeddings based on the knowledge graph. The device may generate graph embeddings for the SMILE data based on the embeddings, and may encode the SMILE data into a latent space. The device may combine the graph embeddings and the latent space to generate a combined latent-embedding space, and may decode the combined latent-embedding space to generate decoded SMILE data. The device may utilize the decoded SMILE data to train an encoder, and may process source SMILE data, with the trained encoder, to generate a source combined latent-embedding space. The device may search the source combined latent-embedding space to identify new SMILE data, and may decode the new SMILE data to generate decoded new SMILE data. The device may evaluate the decoded new SMILE data to identify particular SMILE data associated with a new compound.

Transferring information through knowledge graph embeddings

Complex computer system architectures are described for analyzing data elements of a knowledge graph, and predicting new surprising or unforeseen facts from relational learning applied to the knowledge graph. This discovery process takes advantage of the knowledge graph structure to improve the computing capabilities of a device executing a discovery calculation by applying both training and inference analysis techniques on the knowledge graph within an embedding space, and generating a scoring strategy for predicting surprising facts that may be discoverable from the knowledge graph.

System to predict surprising links in knowledge graphs

Complex computer system architectures are described for utilizing a knowledge data graph comprised of elements, and selecting a discovery element to replace an existing element of a formulation depicted in the knowledge data graph. The substitution process takes advantage of the knowledge data graph structure to improve the computing capabilities of a computing device executing a substitution calculation by translating the knowledge data graph into an embedding space, and determining a discovery element from within the embedding space.

Compound discovery via information divergence with knowledge graphs

Knowledge graph systems are disclosed for enhancing a knowledge graph by generating a new node. The knowledge graph system converts a knowledge graph into an embedding space, and selects a region of interest from within the embedding space. The knowledge graph system further identifies, from the region of interest, one or more gap regions, and calculates a center for each gap region. A node is generated for each gap region, and the information represented by the node is added to the original knowledge graph to generate an updated knowledge graph.

Density-based computation for information discovery in knowledge graphs

The present disclosure relates to systems, methods, and products for identifying candidate molecule. The system includes a non-transitory memory storing instructions; and a processor in communication with the non-transitory memory. The processor executes the instructions to cause the system to receive drug data; convert the drug data into at least one point in a latent space using a grammar variational auto-encoder (VAE) model; receive a query for the at least one candidate molecule; select one or more points in the latent space; and create a k-dimensional tree graph based on the query for the at least one candidate molecule and the selected one or more points; determine a plurality of paths according to an interpolation technique; receive preference data; determine an optimum path; determine at least one candidate point on the optimum path; and determine a drug molecular structure using an inverse of the grammar VAE model.

Denovo generation of molecules using manifold traversal

A device may generate a synthetic knowledge graph based on a true knowledge graph, may partition the synthetic knowledge graph into a set of synthetic data partitions, and may determine, using a plurality of teacher models, an aggregated prediction. The aggregated prediction may be based on individual predictions from corresponding individual teacher models included in the plurality of teacher models. The device may determine, using a student model and based on the synthetic knowledge graph and noise, a student prediction. The student model may be trained based on historical synthetic knowledge graphs and historical aggregated predictions associated with the plurality of teacher models. The device may determine an error metric based on the aggregated prediction and the student prediction, and may perform an action associated with the synthetic knowledge graph based on the error metric.

Differentially private dataset generation and modeling for knowledge graphs

A system detecting counterfeit items based on machine learning and analysis of visual and textual data is disclosed. The system may comprise a data access interface to receive product data associated with a protected product from a user device. The product data may comprise multimodal data that describes the protected product. The system may also comprise a search term generator to generate search terms based on the received product data. The system may comprise a processor to identify one or more potential counterfeit items from the at least one web source using a crawling technique to obtain data associated with to similar products from the at least one web source, identifying at least one match for similar products, and using image processing and analysis to determine if the at least one match for similar products comprises at least one or more potential counterfeit items. The system may also generate a takedown notice to the at least one web source if a user confirms the one or more potential counterfeit is items.

Detection of counterfeit items based on machine learning and analysis of visual and textual data

A method of updating a plurality of databases storing place names includes collecting first, second and third coordinates of addresses including a first, a second and a third place name, respectively, clustering the first, second and third coordinates in a first, a second and a third cluster, respectively, obtaining a base address including the first place name and having base coordinates, assessing the best fit of the base coordinates to the first, second, and third clusters, and based on the two best fit clusters, identifying the second or third place name as an alias place name for the first place name. The method further includes obtaining alias addresses including the alias place name, fuzzy matching the base address to an alias address, and updating first and second databases by linking the alias address in the second database to the base address in the first database.

Generating a ranked list of best fitting place names

A device may receive individual information associated with individual activities of an individual, and may aggregate the individual information, based on a time period, to generate aggregated individual information. The device may identify patterns in the aggregated individual information, and may determine states for the patterns based on state information associated with activities capable of being performed by individuals. The device may generate a sequential knowledge graph based on modifying a knowledge graph with the states and adding a sequence of activities to the knowledge graph, and may determine embeddings for the individual activities based on the sequential knowledge graph. The device may determine anonymized activity signatures for the individual activities based on the embeddings, and may combine the anonymized activity signatures to generate a time-based anonymized activity signature for the individual, wherein the time-based anonymized activity signature providing information that may be utilized without divulging the individual information.

Determining anonymized temporal activity signatures of individuals

A solution for managing an asset, such as a movable asset, deployed to a fixed location, is provided. Location and/or power information is periodically acquired for the asset and assessed to determine whether the asset has moved from a desired physical area and/or the power is insufficient to continue with planned operations. In response to either condition, the operation of various devices on the asset can be adjusted to minimize an inferred risk caused by the asset moving outside of the physical area and/or maximize an inferred probability of recovering the asset.

Growing community of inventors

Dublin, Ireland

Jeremiah Patrick Hayes